






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						UpperCamelCase__						:      str								=					"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_=None,  snake_case_=None  )   ->  Any:


	"""simple docstring"""



	if attention_mask is None:
		a		=			np.where(input_ids != config.pad_token_id,  1,  0  )
	return {
	    "input_ids": input_ids,
	    "attention_mask": attention_mask,
	}







@require_flax
class 				lowerCamelCase_     :
							def __init__(						self		:					Tuple							,__lowerCamelCase		:					Union[str, Any]							,__lowerCamelCase		:					Optional[Any]=13							,__lowerCamelCase		:					Tuple=7							,__lowerCamelCase		:					Tuple=True							,__lowerCamelCase		:					Any=False							,__lowerCamelCase		:					int=99							,__lowerCamelCase		:					Dict=16							,__lowerCamelCase		:					Optional[Any]=2							,__lowerCamelCase		:					Optional[int]=4							,__lowerCamelCase		:					List[str]=4							,__lowerCamelCase		:					Dict="gelu"							,__lowerCamelCase		:					str=0.1							,__lowerCamelCase		:					Union[str, Any]=0.1							,__lowerCamelCase		:					int=20							,__lowerCamelCase		:					Dict=2							,__lowerCamelCase		:					Dict=1							,__lowerCamelCase		:					Tuple=0							,__lowerCamelCase		:					Optional[Any]=16							,__lowerCamelCase		:					Tuple=16							,__lowerCamelCase		:					List[Any]=0.02							,):





								'''simple docstring'''



								a		=			parent
								a		=			batch_size
								a		=			seq_length
								a		=			is_training
								a		=			use_labels
								a		=			vocab_size
								a		=			hidden_size
								a		=			num_hidden_layers
								a		=			num_attention_heads
								a		=			intermediate_size
								a		=			hidden_act
								a		=			hidden_dropout_prob
								a		=			attention_probs_dropout_prob
								a		=			max_position_embeddings
								a		=			eos_token_id
								a		=			pad_token_id
								a		=			bos_token_id
								a		=			embed_dim
								a		=			word_embed_proj_dim
								a		=			initializer_range
								a		=			False
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								a		=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,self.vocab_size					)							,3							,self.vocab_size					)
								a		=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,dtype=np.intaa					))							,-1					)

								a		=			OPTConfig(
								    vocab_size=self.vocab_size							,hidden_size=self.hidden_size							,num_hidden_layers=self.num_hidden_layers							,num_attention_heads=self.num_attention_heads							,ffn_dim=self.intermediate_size							,dropout=self.hidden_dropout_prob							,attention_dropout=self.attention_probs_dropout_prob							,max_position_embeddings=self.max_position_embeddings							,eos_token_id=self.eos_token_id							,bos_token_id=self.bos_token_id							,pad_token_id=self.pad_token_id							,embed_dim=self.embed_dim							,is_encoder_decoder=__lowerCamelCase							,word_embed_proj_dim=self.word_embed_proj_dim							,initializer_range=self.initializer_range							,use_cache=__lowerCamelCase							,)
								a		=			prepare_opt_inputs_dict(__lowerCamelCase							,__lowerCamelCase					)
								return config, inputs_dict
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								a	, a		=			self.prepare_config_and_inputs()
								return config, inputs_dict
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]							,__lowerCamelCase		:					Optional[int]							,__lowerCamelCase		:					List[Any]							,__lowerCamelCase		:					str					):





								'''simple docstring'''



								a		=			20
								a		=			model_class_name(__lowerCamelCase					)

								a		=			inputs_dict['''input_ids''']
								a		=			inputs_dict['''attention_mask''']

								a		=			model.init_cache(input_ids.shape[0]							,__lowerCamelCase					)
								a		=			jnp.ones((input_ids.shape[0], max_length)							,dtype='''i4'''					)

								a		=			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,(input_ids.shape[0], input_ids.shape[-1] - 1)							,)
								a		=			model(
								    input_ids[:, :-1]							,attention_mask=__lowerCamelCase							,past_key_values=__lowerCamelCase							,position_ids=__lowerCamelCase							,)

								a		=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,dtype='''i4'''					)
								a		=			model(
								    input_ids[:, -1:]							,attention_mask=__lowerCamelCase							,past_key_values=outputs_cache.past_key_values							,position_ids=__lowerCamelCase							,)

								a		=			model(__lowerCamelCase					)

								a		=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
								self.parent.assertTrue(diff < 1e-3							,msg=F"""Max diff is {diff}"""					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					int							,__lowerCamelCase		:					Any							,__lowerCamelCase		:					List[str]							,__lowerCamelCase		:					List[Any]					):





								'''simple docstring'''



								a		=			20
								a		=			model_class_name(__lowerCamelCase					)

								a	, a		=			(
								    inputs_dict['''input_ids'''],
								    inputs_dict['''attention_mask'''],
								)

								a		=			jnp.concatenate(
								    [
								        attention_mask,
								        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
								    ]							,axis=-1							,)

								a		=			model.init_cache(input_ids.shape[0]							,__lowerCamelCase					)
								a		=			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,(input_ids.shape[0], input_ids.shape[-1] - 1)							,)

								a		=			model(
								    input_ids[:, :-1]							,attention_mask=__lowerCamelCase							,past_key_values=__lowerCamelCase							,position_ids=__lowerCamelCase							,)
								a		=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,dtype='''i4'''					)
								a		=			model(
								    input_ids[:, -1:]							,past_key_values=outputs_cache.past_key_values							,attention_mask=__lowerCamelCase							,position_ids=__lowerCamelCase							,)

								a		=			model(__lowerCamelCase							,attention_mask=__lowerCamelCase					)

								a		=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
								self.parent.assertTrue(diff < 1e-3							,msg=F"""Max diff is {diff}"""					)







@require_flax
class 				lowerCamelCase_     (	a_ ,						unittest.TestCase ,						a_     ):
							SCREAMING_SNAKE_CASE_						     =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
							SCREAMING_SNAKE_CASE_						     =			() if is_flax_available() else ()
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								a		=			FlaxOPTModelTester(self					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)




							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								for model_class_name in self.all_model_classes:
									a		=			model_class_name.from_pretrained('''facebook/opt-125m'''					)
									a		=			np.ones((1, 1)					) * model.config.eos_token_id
									a		=			model(__lowerCamelCase					)
									self.assertIsNotNone(__lowerCamelCase					)







@require_sentencepiece
@require_flax
class 				lowerCamelCase_     (	unittest.TestCase     ):
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
								a		=			jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]					)
								a		=			model(input_ids=__lowerCamelCase					).last_hidden_state
								a		=			(1, 11, 5_12)
								self.assertEqual(output.shape							,__lowerCamelCase					)
								a		=			jnp.array(
								    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]					)
								self.assertTrue(jnp.allclose(output[:, :3, :3]							,__lowerCamelCase							,atol=4e-2					)					)







@require_flax
@slow
class 				lowerCamelCase_     (	unittest.TestCase     ):
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								super().setUp()
								a		=			'''facebook/opt-350m'''




							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								a		=			FlaxOPTForCausalLM.from_pretrained(self.path_model					)
								a		=			GPTaTokenizer.from_pretrained(self.path_model					)

								a		=			[
								    '''Today is a beautiful day and I want to''',
								    '''In the city of''',
								    '''Paris is the capital of France and''',
								    '''Computers and mobile phones have taken''',
								]
								# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
								a		=			tokenizer(__lowerCamelCase							,return_tensors='''jax'''							,padding=__lowerCamelCase							,add_special_tokens=__lowerCamelCase					)
								a		=			model(inputs.input_ids							,attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
								a		=			jnp.array(
								    [
								        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
								        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
								        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
								        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
								    ]					)
								self.assertTrue(jnp.allclose(__lowerCamelCase							,__lowerCamelCase							,atol=4e-2					)					)

								a		=			jax.jit(__lowerCamelCase					)
								a		=			model(inputs.input_ids							,attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
								self.assertTrue(jnp.allclose(__lowerCamelCase							,__lowerCamelCase							,atol=4e-2					)					)







@require_flax
@slow
class 				lowerCamelCase_     (	unittest.TestCase     ):
							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								return [
								    "Today is a beautiful day and I want",
								    "In the city of",
								    "Paris is the capital of France and",
								    "Computers and mobile phones have taken",
								]
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								a		=			'''facebook/opt-125m'''

								a		=			[
								    '''Today is a beautiful day and I want to''',
								    '''In the city of New York, the city''',
								    '''Paris is the capital of France and the capital''',
								    '''Computers and mobile phones have taken over the''',
								]

								a		=			[]

								a		=			FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase					)
								a		=			GPTaTokenizer.from_pretrained(__lowerCamelCase					)

								for prompt in self.prompts:
									a		=			tokenizer(__lowerCamelCase							,return_tensors='''jax'''					).input_ids

									a		=			model.generate(__lowerCamelCase							,max_length=10					)
									a		=			generated_ids[0]

									a		=			tokenizer.batch_decode(__lowerCamelCase							,skip_special_tokens=__lowerCamelCase					)
									predicted_outputs += generated_string

								self.assertListEqual(__lowerCamelCase							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								a		=			'''facebook/opt-350m'''

								a		=			[
								    '''Today is a beautiful day and I want to''',
								    '''In the city of San Francisco, the city''',
								    '''Paris is the capital of France and the capital''',
								    '''Computers and mobile phones have taken over the''',
								]

								a		=			[]
								a		=			FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase					)
								a		=			GPTaTokenizer.from_pretrained(__lowerCamelCase					)

								for prompt in self.prompts:
									a		=			tokenizer(__lowerCamelCase							,return_tensors='''jax'''					).input_ids

									a		=			model.generate(__lowerCamelCase							,max_length=10					)
									a		=			generated_ids[0]

									a		=			tokenizer.batch_decode(__lowerCamelCase							,skip_special_tokens=__lowerCamelCase					)
									predicted_outputs += generated_string

								self.assertListEqual(__lowerCamelCase							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			'''facebook/opt-125m'''
								a		=			[
								    '''Today is a beautiful day and I want to thank''',
								    '''In the city of Rome Canaver Canaver Canaver Canaver''',
								]
								a		=			FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase					)
								a		=			GPTaTokenizer.from_pretrained(__lowerCamelCase					)
								a		=			tokenizer(
								    [
								        '''Today is a beautiful day and I want to''',
								        '''In the city of''',
								    ]							,return_tensors='''jax'''							,padding=__lowerCamelCase							,)

								a		=			jax.jit(model.generate					)

								a		=			jit_generate(inputs['''input_ids''']							,attention_mask=inputs['''attention_mask''']					).sequences

								a		=			tokenizer.batch_decode(__lowerCamelCase							,skip_special_tokens=__lowerCamelCase					)

								self.assertIsNotNone(__lowerCamelCase							,__lowerCamelCase					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								a		=			'''facebook/opt-350m'''

								a		=			GPTaTokenizer.from_pretrained(__lowerCamelCase					)
								a		=			FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase					)

								a		=			'''left'''

								# use different length sentences to test batching
								a		=			[
								    '''Hello, my dog is a little''',
								    '''Today, I''',
								]

								a		=			tokenizer(__lowerCamelCase							,return_tensors='''jax'''							,padding=__lowerCamelCase					)
								a		=			inputs['''input_ids''']

								a		=			model.generate(input_ids=__lowerCamelCase							,attention_mask=inputs['''attention_mask''']							,trace=__lowerCamelCase					)

								a		=			tokenizer(sentences[0]							,return_tensors='''jax'''					).input_ids
								a		=			model.generate(input_ids=__lowerCamelCase					)

								a		=			inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
								a		=			tokenizer(sentences[1]							,return_tensors='''jax'''					).input_ids
								a		=			model.generate(input_ids=__lowerCamelCase							,max_length=model.config.max_length - num_paddings					)

								a		=			tokenizer.batch_decode(outputs[0]							,skip_special_tokens=__lowerCamelCase					)
								a		=			tokenizer.decode(output_non_padded[0][0]							,skip_special_tokens=__lowerCamelCase					)
								a		=			tokenizer.decode(output_padded[0][0]							,skip_special_tokens=__lowerCamelCase					)

								a		=			[
								    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
								    '''Today, I was in the middle of a conversation with a friend about the''',
								]
								self.assertListEqual(__lowerCamelCase							,__lowerCamelCase					)
								self.assertListEqual(__lowerCamelCase							,[non_padded_sentence, padded_sentence]					)













import numpy as np





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_="utf-8",  snake_case_="surrogatepass"  )   ->  List[str]:


	"""simple docstring"""



	a		=			bin(int.from_bytes(text.encode(snake_case_,  snake_case_  ),  '''big'''  )  )[2:]
	return bits.zfill(8 * ((len(snake_case_  ) + 7) // 8)  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_="utf-8",  snake_case_="surrogatepass"  )   ->  int:


	"""simple docstring"""



	a		=			int(snake_case_,  2  )
	return n.to_bytes((n.bit_length() + 7) // 8,  '''big'''  ).decode(snake_case_,  snake_case_  ) or "\0"





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  List[Any]:


	"""simple docstring"""



	if size_par + len(snake_case_  ) <= 2**size_par - (len(snake_case_  ) - 1):
		raise ValueError('''size of parity don\'t match with size of data'''  )

	a		=			[]
	a		=			[]
	a		=			[bin(snake_case_  )[2:] for x in range(1,  size_par + len(snake_case_  ) + 1  )]

	# sorted information data for the size of the output data
	a		=			[]
	# data position template + parity
	a		=			[]
	# parity bit counter
	a		=			0
	# counter position of data bits
	a		=			0

	for x in range(1,  size_par + len(snake_case_  ) + 1  ):
		# Performs a template of bit positions - who should be given,
		# and who should be parity
		if qtd_bp < size_par:
			if (np.log(snake_case_  ) / np.log(2  )).is_integer():
				data_out_gab.append('''P'''  )
				a		=			qtd_bp + 1
			else:
				data_out_gab.append('''D'''  )
		else:
			data_out_gab.append('''D'''  )

		# Sorts the data to the new output size
		if data_out_gab[-1] == "D":
			data_ord.append(data[cont_data]  )
			cont_data += 1
		else:
			data_ord.append(snake_case_  )

    # Calculates parity
	a		=			0  # parity bit counter
	for bp in range(1,  size_par + 1  ):
		# Bit counter one for a given parity
		a		=			0
		# counter to control the loop reading
		a		=			0
		for x in data_ord:
			if x is not None:
				try:
					a		=			(bin_pos[cont_loop])[-1 * (bp)]
				except IndexError:
					a		=			'''0'''
				if aux == "1" and x == "1":
					cont_bo += 1
			cont_loop += 1
		parity.append(cont_bo % 2  )

		qtd_bp += 1

	# Mount the message
	a		=			0  # parity bit counter
	for x in range(0,  size_par + len(snake_case_  )  ):
		if data_ord[x] is None:
			data_out.append(str(parity[cont_bp]  )  )
			cont_bp += 1
		else:
			data_out.append(data_ord[x]  )

	return data_out





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	a		=			[]
	# Parity bit counter
	a		=			0
	# Counter p data bit reading
	a		=			0
	# list of parity received
	a		=			[]
	a		=			[]

	for x in range(1,  len(snake_case_  ) + 1  ):
		# Performs a template of bit positions - who should be given,
		#  and who should be parity
		if qtd_bp < size_par and (np.log(snake_case_  ) / np.log(2  )).is_integer():
			data_out_gab.append('''P'''  )
			a		=			qtd_bp + 1
		else:
			data_out_gab.append('''D'''  )

		# Sorts the data to the new output size
		if data_out_gab[-1] == "D":
			data_output.append(data[cont_data]  )
		else:
			parity_received.append(data[cont_data]  )
		cont_data += 1

	# -----------calculates the parity with the data
	a		=			[]
	a		=			[]
	a		=			[bin(snake_case_  )[2:] for x in range(1,  size_par + len(snake_case_  ) + 1  )]

	#  sorted information data for the size of the output data
	a		=			[]
	# Data position feedback + parity
	a		=			[]
	# Parity bit counter
	a		=			0
	# Counter p data bit reading
	a		=			0

	for x in range(1,  size_par + len(snake_case_  ) + 1  ):
		# Performs a template position of bits - who should be given,
		# and who should be parity
		if qtd_bp < size_par and (np.log(snake_case_  ) / np.log(2  )).is_integer():
			data_out_gab.append('''P'''  )
			a		=			qtd_bp + 1
		else:
			data_out_gab.append('''D'''  )

		# Sorts the data to the new output size
		if data_out_gab[-1] == "D":
			data_ord.append(data_output[cont_data]  )
			cont_data += 1
		else:
			data_ord.append(snake_case_  )

    # Calculates parity
	a		=			0  # parity bit counter
	for bp in range(1,  size_par + 1  ):
		# Bit counter one for a certain parity
		a		=			0
		# Counter to control loop reading
		a		=			0
		for x in data_ord:
			if x is not None:
				try:
					a		=			(bin_pos[cont_loop])[-1 * (bp)]
				except IndexError:
					a		=			'''0'''
				if aux == "1" and x == "1":
					cont_bo += 1
			cont_loop += 1
		parity.append(str(cont_bo % 2  )  )

		qtd_bp += 1

	# Mount the message
	a		=			0  # Parity bit counter
	for x in range(0,  size_par + len(snake_case_  )  ):
		if data_ord[x] is None:
			data_out.append(str(parity[cont_bp]  )  )
			cont_bp += 1
		else:
			data_out.append(data_ord[x]  )

	a		=			parity_received == parity
	return data_output, ack





