




'''simple docstring'''







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
      import torch

class       lowerCamelCase_							:






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase=14     ,						__UpperCamelCase=7     ,						__UpperCamelCase=True     ,						__UpperCamelCase=True     ,						__UpperCamelCase=False     ,						__UpperCamelCase=True     ,						__UpperCamelCase=99     ,						__UpperCamelCase=32     ,						__UpperCamelCase=4     ,						__UpperCamelCase=4     ,						__UpperCamelCase=[[["global", "local"], 2]]     ,						__UpperCamelCase=37     ,						__UpperCamelCase="gelu"     ,						__UpperCamelCase=0.1     ,						__UpperCamelCase=0.1     ,						__UpperCamelCase=5_12     ,						__UpperCamelCase=7     ,						__UpperCamelCase=0.02     ,						):
       UpperCamelCase__      =			parent
       UpperCamelCase__      =			batch_size
       UpperCamelCase__      =			seq_length
       UpperCamelCase__      =			is_training
       UpperCamelCase__      =			use_input_mask
       UpperCamelCase__      =			use_token_type_ids
       UpperCamelCase__      =			use_labels
       UpperCamelCase__      =			vocab_size
       UpperCamelCase__      =			hidden_size
       UpperCamelCase__      =			num_hidden_layers
       UpperCamelCase__      =			num_attention_heads
       UpperCamelCase__      =			attention_types
       UpperCamelCase__      =			intermediate_size
       UpperCamelCase__      =			hidden_act
       UpperCamelCase__      =			hidden_dropout_prob
       UpperCamelCase__      =			attention_probs_dropout_prob
       UpperCamelCase__      =			max_position_embeddings
       UpperCamelCase__      =			window_size
       UpperCamelCase__      =			initializer_range
       UpperCamelCase__      =			None
       UpperCamelCase__      =			vocab_size - 1
       UpperCamelCase__      =			vocab_size - 1
       UpperCamelCase__      =			vocab_size - 1


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size )

       UpperCamelCase__      =			None
       if self.use_input_mask:
           UpperCamelCase__      =			random_attention_mask([self.batch_size, self.seq_length] )

       UpperCamelCase__      =			GPTNeoConfig(
           vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						num_layers=self.num_hidden_layers     ,						num_heads=self.num_attention_heads     ,						max_position_embeddings=self.max_position_embeddings     ,						use_cache=__UpperCamelCase     ,						bos_token_id=self.bos_token_id     ,						eos_token_id=self.eos_token_id     ,						pad_token_id=self.pad_token_id     ,						window_size=self.window_size     ,						attention_types=self.attention_types     ,						)

       return (config, input_ids, input_mask)


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.prepare_config_and_inputs()
       UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__      =			config_and_inputs
       UpperCamelCase__      =			{"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			20
       UpperCamelCase__      =			model_class_name(__UpperCamelCase )

       UpperCamelCase__      =			model.init_cache(input_ids.shape[0]     ,						__UpperCamelCase )
       UpperCamelCase__      =			jnp.ones((input_ids.shape[0], max_decoder_length)     ,						dtype="i4" )

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

       UpperCamelCase__      =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,						dtype="i4" )
       UpperCamelCase__      =			model(
           input_ids[:, -1:]     ,						attention_mask=__UpperCamelCase     ,						past_key_values=outputs_cache.past_key_values     ,						position_ids=__UpperCamelCase     ,						)

       UpperCamelCase__      =			model(__UpperCamelCase )

       UpperCamelCase__      =			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 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			20
       UpperCamelCase__      =			model_class_name(__UpperCamelCase )

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

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

       UpperCamelCase__      =			model(
           input_ids[:, :-1]     ,						attention_mask=__UpperCamelCase     ,						past_key_values=__UpperCamelCase     ,						position_ids=__UpperCamelCase     ,						)
       UpperCamelCase__      =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,						dtype="i4" )
       UpperCamelCase__      =			model(
           input_ids[:, -1:]     ,						past_key_values=outputs_cache.past_key_values     ,						attention_mask=__UpperCamelCase     ,						position_ids=__UpperCamelCase     ,						)

       UpperCamelCase__      =			model(__UpperCamelCase     ,						attention_mask=__UpperCamelCase )

       UpperCamelCase__      =			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_							(	lowerCAmelCase_					,    lowerCAmelCase_					,    unittest.TestCase  ):






   '''simple docstring'''

   A :     List[Any]				=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   A :     Union[str, Any]				=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			FlaxGPTNeoModelTester(self )


   def 	_UpperCamelCase       (  self ):
       for model_class_name in self.all_model_classes:
           UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       for model_class_name in self.all_model_classes:
           UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               __UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase )


   @slow
   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			GPTaTokenizer.from_pretrained("gpt2"     ,						pad_token="<|endoftext|>"     ,						padding_side="left" )
       UpperCamelCase__      =			tokenizer(["Hello this is a long string", "Hey"]     ,						return_tensors="np"     ,						padding=__UpperCamelCase     ,						truncation=__UpperCamelCase )

       UpperCamelCase__      =			FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M" )
       UpperCamelCase__      =			False
       UpperCamelCase__      =			model.config.eos_token_id

       UpperCamelCase__      =			jax.jit(model.generate )

       UpperCamelCase__      =			jit_generate(
           inputs["input_ids"]     ,						attention_mask=inputs["attention_mask"]     ,						pad_token_id=tokenizer.pad_token_id ).sequences

       UpperCamelCase__      =			tokenizer.batch_decode(__UpperCamelCase     ,						skip_special_tokens=__UpperCamelCase )

       UpperCamelCase__      =			[
           "Hello this is a long string of text.\n\nI'm trying to get the text of the",
           "Hey, I'm a little late to the party. I'm going to",
       ]

       self.assertListEqual(__UpperCamelCase     ,						__UpperCamelCase )


   @is_pt_flax_cross_test
   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__    ,  UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__ ):
               # prepare inputs
               UpperCamelCase__      =			self._prepare_for_class(__UpperCamelCase     ,						__UpperCamelCase )
               UpperCamelCase__      =			{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

               # load corresponding PyTorch class
               UpperCamelCase__      =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
               UpperCamelCase__      =			getattr(__UpperCamelCase     ,						__UpperCamelCase )

               UpperCamelCase__    ,  UpperCamelCase__      =			pt_inputs["input_ids"].shape
               UpperCamelCase__      =			np.random.randint(0     ,						seq_length - 1     ,						size=(batch_size,) )
               for batch_idx, start_index in enumerate(__UpperCamelCase ):
                   UpperCamelCase__      =			0
                   UpperCamelCase__      =			1
                   UpperCamelCase__      =			0
                   UpperCamelCase__      =			1
               UpperCamelCase__      =			pt_model_class(__UpperCamelCase ).eval()
               UpperCamelCase__      =			model_class(__UpperCamelCase     ,						dtype=jnp.floataa )

               UpperCamelCase__      =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,						__UpperCamelCase )
               UpperCamelCase__      =			fx_state

               with torch.no_grad():
                   UpperCamelCase__      =			pt_model(**__UpperCamelCase ).to_tuple()

               UpperCamelCase__      =			fx_model(**__UpperCamelCase ).to_tuple()
               self.assertEqual(len(__UpperCamelCase )     ,						len(__UpperCamelCase )     ,						"Output lengths differ between Flax and PyTorch" )
               for fx_output, pt_output in zip(__UpperCamelCase     ,						__UpperCamelCase ):
                   self.assert_almost_equals(fx_output[:, -1]     ,						pt_output[:, -1].numpy()     ,						4e-2 )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   pt_model.save_pretrained(__UpperCamelCase )
                   UpperCamelCase__      =			model_class.from_pretrained(__UpperCamelCase     ,						from_pt=__UpperCamelCase )

               UpperCamelCase__      =			fx_model_loaded(**__UpperCamelCase ).to_tuple()
               self.assertEqual(
                   len(__UpperCamelCase )     ,						len(__UpperCamelCase )     ,						"Output lengths differ between Flax and PyTorch" )
               for fx_output_loaded, pt_output in zip(__UpperCamelCase     ,						__UpperCamelCase ):
                   self.assert_almost_equals(fx_output_loaded[:, -1]     ,						pt_output[:, -1].numpy()     ,						4e-2 )


   @is_pt_flax_cross_test
   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__    ,  UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__ ):
               # prepare inputs
               UpperCamelCase__      =			self._prepare_for_class(__UpperCamelCase     ,						__UpperCamelCase )
               UpperCamelCase__      =			{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

               # load corresponding PyTorch class
               UpperCamelCase__      =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
               UpperCamelCase__      =			getattr(__UpperCamelCase     ,						__UpperCamelCase )

               UpperCamelCase__      =			pt_model_class(__UpperCamelCase ).eval()
               UpperCamelCase__      =			model_class(__UpperCamelCase     ,						dtype=jnp.floataa )

               UpperCamelCase__      =			load_flax_weights_in_pytorch_model(__UpperCamelCase     ,						fx_model.params )
               UpperCamelCase__    ,  UpperCamelCase__      =			pt_inputs["input_ids"].shape
               UpperCamelCase__      =			np.random.randint(0     ,						seq_length - 1     ,						size=(batch_size,) )
               for batch_idx, start_index in enumerate(__UpperCamelCase ):
                   UpperCamelCase__      =			0
                   UpperCamelCase__      =			1
                   UpperCamelCase__      =			0
                   UpperCamelCase__      =			1

               # make sure weights are tied in PyTorch
               pt_model.tie_weights()

               with torch.no_grad():
                   UpperCamelCase__      =			pt_model(**__UpperCamelCase ).to_tuple()

               UpperCamelCase__      =			fx_model(**__UpperCamelCase ).to_tuple()
               self.assertEqual(len(__UpperCamelCase )     ,						len(__UpperCamelCase )     ,						"Output lengths differ between Flax and PyTorch" )
               for fx_output, pt_output in zip(__UpperCamelCase     ,						__UpperCamelCase ):
                   self.assert_almost_equals(fx_output[:, -1]     ,						pt_output[:, -1].numpy()     ,						4e-2 )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   fx_model.save_pretrained(__UpperCamelCase )
                   UpperCamelCase__      =			pt_model_class.from_pretrained(__UpperCamelCase     ,						from_flax=__UpperCamelCase )

               with torch.no_grad():
                   UpperCamelCase__      =			pt_model_loaded(**__UpperCamelCase ).to_tuple()

               self.assertEqual(
                   len(__UpperCamelCase )     ,						len(__UpperCamelCase )     ,						"Output lengths differ between Flax and PyTorch" )
               for fx_output, pt_output in zip(__UpperCamelCase     ,						__UpperCamelCase ):
                   self.assert_almost_equals(fx_output[:, -1]     ,						pt_output[:, -1].numpy()     ,						4e-2 )







   @slow
   def 	_UpperCamelCase       (  self ):
       for model_class_name in self.all_model_classes:
           UpperCamelCase__      =			model_class_name.from_pretrained("EleutherAI/gpt-neo-125M" )
           UpperCamelCase__      =			model(np.ones((1, 1) ) )
           self.assertIsNotNone(__UpperCamelCase )









'''simple docstring'''







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types

class       lowerCamelCase_							(	unittest.TestCase					,    lowerCAmelCase_  ):






   '''simple docstring'''

   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			load_tool("translation" )
       self.tool.setup()
       UpperCamelCase__      =			load_tool("translation"     ,						remote=__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.remote_tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.remote_tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       # There is a single output
       if len(self.tool.outputs ) == 1:
           UpperCamelCase__      =			[outputs]

       self.assertListEqual(output_types(__UpperCamelCase )     ,						self.tool.outputs )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       if not isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           UpperCamelCase__      =			[outputs]

       self.assertEqual(len(__UpperCamelCase )     ,						len(self.tool.outputs ) )

       for output, output_type in zip(__UpperCamelCase     ,						self.tool.outputs ):
           UpperCamelCase__      =			AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(__UpperCamelCase     ,						__UpperCamelCase ) )







   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]

       UpperCamelCase__      =			[]

       for _input, input_type in zip(__UpperCamelCase     ,						self.tool.inputs ):
           if isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
               _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
           else:
               _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       if not isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           UpperCamelCase__      =			[outputs]

       self.assertEqual(len(__UpperCamelCase )     ,						len(self.tool.outputs ) )




