

"""simple docstring"""






from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


lowerCamelCase__				:  int													=  logging.get_logger(__name__)






class     _UpperCAmelCase			(		nn.Module):
			__a :       HybridCLIPConfig
			__a :       jnp.dtype									=							jnp.floataa
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							int									=					self.config.text_config
				_UpperCAmelCase    :							Union[str, Any]									=					self.config.vision_config

				_UpperCAmelCase    :							Optional[Any]									=					self.config.projection_dim
				_UpperCAmelCase    :							Optional[Any]									=					text_config.hidden_size
				_UpperCAmelCase    :							Tuple									=					vision_config.hidden_size

				_UpperCAmelCase    :							Optional[int]									=					FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
				_UpperCAmelCase    :							List[str]									=					FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__      ,		_A     ).module_class

				_UpperCAmelCase    :							Optional[int]									=					text_module(_A      ,		dtype=self.dtype     )
				_UpperCAmelCase    :							List[str]									=					vision_module(_A      ,		dtype=self.dtype     )

				_UpperCAmelCase    :							Any									=					nn.Dense(
				    self.projection_dim      ,		dtype=self.dtype      ,		kernel_init=jax.nn.initializers.normal(0.02     )      ,		use_bias=_A      ,		)
				_UpperCAmelCase    :							List[Any]									=					nn.Dense(
				    self.projection_dim      ,		dtype=self.dtype      ,		kernel_init=jax.nn.initializers.normal(0.02     )      ,		use_bias=_A      ,		)
				_UpperCAmelCase    :							Tuple									=					self.param("""logit_scale"""      ,		jax.nn.initializers.ones      ,		[]     )
			def __call__(		self      ,		_A=None      ,		_A=None      ,		_A=None      ,		_A=None      ,		_A=None      ,		_A = True      ,		_A=None      ,		_A=None      ,		_A=None      ,		)    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					return_dict if return_dict is not None else self.config.return_dict

				_UpperCAmelCase    :							Optional[int]									=					self.vision_model(
				    pixel_values=_A      ,		deterministic=_A      ,		output_attentions=_A      ,		output_hidden_states=_A      ,		return_dict=_A      ,		)

				_UpperCAmelCase    :							List[str]									=					self.text_model(
				    input_ids=_A      ,		attention_mask=_A      ,		token_type_ids=_A      ,		position_ids=_A      ,		deterministic=_A      ,		output_attentions=_A      ,		output_hidden_states=_A      ,		return_dict=_A      ,		)

				_UpperCAmelCase    :							Union[str, Any]									=					vision_outputs[1]
				_UpperCAmelCase    :							Tuple									=					self.visual_projection(_A     )

				_UpperCAmelCase    :							Union[str, Any]									=					text_outputs[1]
				_UpperCAmelCase    :							Optional[Any]									=					self.text_projection(_A     )

				# normalized features
				_UpperCAmelCase    :							List[Any]									=					image_embeds / jnp.linalg.norm(_A      ,		axis=-1      ,		keepdims=_A     )
				_UpperCAmelCase    :							Union[str, Any]									=					text_embeds / jnp.linalg.norm(_A      ,		axis=-1      ,		keepdims=_A     )

				# cosine similarity as logits
				_UpperCAmelCase    :							Dict									=					jnp.exp(self.logit_scale     )
				_UpperCAmelCase    :							Tuple									=					jnp.matmul(_A      ,		image_embeds.T     ) * logit_scale
				_UpperCAmelCase    :							Optional[int]									=					logits_per_text.T

				if not return_dict:
					return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

				return FlaxCLIPOutput(
				    logits_per_image=_A      ,		logits_per_text=_A      ,		text_embeds=_A      ,		image_embeds=_A      ,		text_model_output=_A      ,		vision_model_output=_A      ,		)






class     _UpperCAmelCase			(		__a):
			__a :       Dict									=							HybridCLIPConfig
			__a :       Union[str, Any]									=							FlaxHybridCLIPModule
			def __init__(		self      ,		_A      ,		_A = None      ,		_A = 0      ,		_A = jnp.floataa      ,		**_A      ,		)    ->    Optional[int]:






				'''simple docstring'''







				if input_shape is None:
					_UpperCAmelCase    :							Optional[Any]									=					((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

				_UpperCAmelCase    :							Any									=					self.module_class(config=_A      ,		dtype=_A      ,		**_A     )
				super().__init__(_A      ,		_A      ,		input_shape=_A      ,		seed=_A      ,		dtype=_A     )
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A = None     )    ->    FrozenDict:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[Any]									=					jnp.zeros(input_shape[0]      ,		dtype="""i4"""     )
				_UpperCAmelCase    :							Tuple									=					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_A     ).shape[-1]     )      ,		input_shape[0]     )
				_UpperCAmelCase    :							int									=					jnp.ones_like(_A     )
				_UpperCAmelCase    :							List[Any]									=					jnp.ones_like(_A     )

				_UpperCAmelCase    :							int									=					jax.random.normal(_A      ,		input_shape[1]     )

				_UpperCAmelCase						,   _UpperCAmelCase    :							Any									=					jax.random.split(_A     )
				_UpperCAmelCase    :							Any									=					{"""params""": params_rng, """dropout""": dropout_rng}

				return self.module.init(_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A     )["params"]
			def __call__(		self      ,		_A      ,		_A      ,		_A=None      ,		_A=None      ,		_A=None      ,		_A = None      ,		_A = None      ,		_A = False      ,		_A = None      ,		_A = None      ,		_A = None      ,		)    ->    Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[Any]									=					output_attentions if output_attentions is not None else self.config.output_attentions
				_UpperCAmelCase    :							str									=					(
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				_UpperCAmelCase    :							Optional[Any]									=					return_dict if return_dict is not None else self.config.return_dict

				if position_ids is None:
					_UpperCAmelCase    :							int									=					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_A     ).shape[-1]     )      ,		input_ids.shape     )

				if token_type_ids is None:
					_UpperCAmelCase    :							Dict									=					jnp.zeros_like(_A     )

				if attention_mask is None:
					_UpperCAmelCase    :							Tuple									=					jnp.ones_like(_A     )

				# Handle any PRNG if needed
				_UpperCAmelCase    :							Tuple									=					{}
				if dropout_rng is not None:
					_UpperCAmelCase    :							List[str]									=					dropout_rng

				return self.module.apply(
				    {"""params""": params or self.params}      ,		jnp.array(_A      ,		dtype="""i4"""     )      ,		jnp.array(_A      ,		dtype=jnp.floataa     )      ,		jnp.array(_A      ,		dtype="""i4"""     )      ,		jnp.array(_A      ,		dtype="""i4"""     )      ,		jnp.array(_A      ,		dtype="""i4"""     )      ,		not train      ,		_A      ,		_A      ,		_A      ,		rngs=_A      ,		)
			def 							__snake_case (		self      ,		_A      ,		_A=None      ,		_A=None      ,		_A=None      ,		_A = None      ,		_A = None      ,		_A=False      ,		)    ->    Tuple:






				'''simple docstring'''







				if position_ids is None:
					_UpperCAmelCase    :							List[Any]									=					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_A     ).shape[-1]     )      ,		input_ids.shape     )

				if token_type_ids is None:
					_UpperCAmelCase    :							int									=					jnp.zeros_like(_A     )

				if attention_mask is None:
					_UpperCAmelCase    :							Optional[Any]									=					jnp.ones_like(_A     )

				# Handle any PRNG if needed
				_UpperCAmelCase    :							Tuple									=					{}
				if dropout_rng is not None:
					_UpperCAmelCase    :							int									=					dropout_rng

				def _get_features(_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A     ):
					_UpperCAmelCase    :							Any									=					module.text_model(
					    input_ids=_A      ,		attention_mask=_A      ,		position_ids=_A      ,		token_type_ids=_A      ,		deterministic=_A      ,		)
					_UpperCAmelCase    :							List[str]									=					text_outputs[1]
					_UpperCAmelCase    :							Union[str, Any]									=					module.text_projection(_A     )
					return text_features

				return self.module.apply(
				    {"""params""": params or self.params}      ,		jnp.array(_A      ,		dtype="""i4"""     )      ,		jnp.array(_A      ,		dtype="""i4"""     )      ,		jnp.array(_A      ,		dtype="""i4"""     )      ,		jnp.array(_A      ,		dtype="""i4"""     )      ,		not train      ,		method=_get_features      ,		rngs=_A      ,		)
			def 							__snake_case (		self      ,		_A      ,		_A = None      ,		_A = None      ,		_A=False     )    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					{}
				if dropout_rng is not None:
					_UpperCAmelCase    :							Dict									=					dropout_rng

				def _get_features(_A      ,		_A      ,		_A     ):
					_UpperCAmelCase    :							Tuple									=					module.vision_model(pixel_values=_A      ,		deterministic=_A     )
					_UpperCAmelCase    :							List[str]									=					vision_outputs[1]  # pooled_output
					_UpperCAmelCase    :							Dict									=					module.visual_projection(_A     )
					return image_features

				return self.module.apply(
				    {"""params""": params or self.params}      ,		jnp.array(_A      ,		dtype=jnp.floataa     )      ,		not train      ,		method=_get_features      ,		rngs=_A      ,		)
			@classmethod
			def 							__snake_case (		cls      ,		_A = None      ,		_A = None      ,		*_A      ,		**_A      ,		)    ->    FlaxPreTrainedModel:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					{
				    argument[len("""text_"""     ) :]: value for argument, value in kwargs.items() if argument.startswith("""text_"""     )
				}

				_UpperCAmelCase    :							int									=					{
				    argument[len("""vision_"""     ) :]: value for argument, value in kwargs.items() if argument.startswith("""vision_"""     )
				}

				# remove text, vision kwargs from kwargs
				for key in kwargs_text.keys():
					del kwargs["text_" + key]
				for key in kwargs_vision.keys():
					del kwargs["vision_" + key]

				# Load and initialize the text and vision model
				_UpperCAmelCase    :							List[Any]									=					kwargs_text.pop("""model"""      ,		_A     )
				if text_model is None:
					assert (
					    text_model_name_or_path is not None
					), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
					from transformers import FlaxAutoModel

					if "config" not in kwargs_text:
						from transformers import AutoConfig

						_UpperCAmelCase    :							Optional[int]									=					AutoConfig.from_pretrained(_A     )
						_UpperCAmelCase    :							Any									=					text_config

					_UpperCAmelCase    :							Dict									=					FlaxAutoModel.from_pretrained(_A      ,		*_A      ,		**_A     )

				_UpperCAmelCase    :							Tuple									=					kwargs_vision.pop("""model"""      ,		_A     )
				if vision_model is None:
					assert (
					    vision_model_name_or_path is not None
					), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
					from transformers import FlaxAutoModel

					if "config" not in kwargs_vision:
						from transformers import AutoConfig

						_UpperCAmelCase    :							Union[str, Any]									=					AutoConfig.from_pretrained(_A     )
						_UpperCAmelCase    :							Dict									=					vision_config

					_UpperCAmelCase    :							Any									=					FlaxAutoModel.from_pretrained(_A      ,		*_A      ,		**_A     )

				# instantiate config with corresponding kwargs
				_UpperCAmelCase    :							List[str]									=					kwargs.pop("""dtype"""      ,		jnp.floataa     )
				_UpperCAmelCase    :							Dict									=					HybridCLIPConfig.from_text_vision_configs(text_model.config      ,		vision_model.config      ,		**_A     )

				# init model
				_UpperCAmelCase    :							Dict									=					cls(_A      ,		*_A      ,		dtype=_A      ,		**_A     )

				if vision_config.model_type == "clip":
					_UpperCAmelCase    :							Dict									=					vision_model.params["""vision_model"""]
					_UpperCAmelCase    :							Union[str, Any]									=					vision_model.params["""visual_projection"""]["""kernel"""]
				else:
					_UpperCAmelCase    :							List[str]									=					vision_model.params

				_UpperCAmelCase    :							Dict									=					text_model.params

				return model








"""simple docstring"""






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowerCamelCase__				:  Any													=  torch.cuda.is_available()
lowerCamelCase__				:  Optional[Any]													=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase__				:  List[str]													=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase__				:  int													=  {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1E-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}
def       UpperCamelCase					(			_lowerCAmelCase     :						Path,  _lowerCAmelCase     :						list )				->			str:
	_UpperCAmelCase    :							Dict									=					"""\n""".join(_lowerCAmelCase )
	Path(_lowerCAmelCase ).open("""w""" ).writelines(_lowerCAmelCase )
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[int] )				->			Optional[Any]:
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.source''' ),  _lowerCAmelCase )
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.target''' ),  _lowerCAmelCase )
	return tmp_dir






class     _UpperCAmelCase			(		__a):
			@classmethod
			def 							__snake_case (		cls     )    ->    Optional[int]:






				'''simple docstring'''







				return cls
			@require_torch_multi_gpu
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					{
				    """no_teacher""": True,
				    """freeze_encoder""": True,
				    """gpus""": 2,
				    """overwrite_output_dir""": True,
				    """sortish_sampler""": True,
				}
				self._test_distiller_cli_fork(_A      ,		check_contents=_A     )
			def 							__snake_case (		self      ,		_A      ,		_A=True     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					{
				    """label_smoothing""": 0.0,
				    """early_stopping_patience""": -1,
				    """train_batch_size""": 1,
				    """eval_batch_size""": 2,
				    """max_epochs""": 2,
				    """alpha_mlm""": 0.2,
				    """alpha_ce""": 0.8,
				    """do_predict""": True,
				    """model_name_or_path""": """sshleifer/tinier_bart""",
				    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
				    """val_check_interval""": 0.5,
				}
				default_updates.update(_A     )
				_UpperCAmelCase    :							dict									=					CHEAP_ARGS.copy()
				_UpperCAmelCase    :							List[Any]									=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
				_UpperCAmelCase    :							Optional[Any]									=					self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=_A      ,		output_dir=_A      ,		**_A     )

				def convert(_A      ,		_A     ):
					if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
						return ""
					if v is False or v is None:
						return ""
					if v is True:  # or len(str(v))==0:
						return f'''--{k}'''
					return f'''--{k}={v}'''

				_UpperCAmelCase    :							List[Any]									=					[x for x in (convert(_A      ,		_A     ) for k, v in args_d.items()) if len(_A     )]
				_UpperCAmelCase    :							Union[str, Any]									=					[sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
				execute_subprocess_async(_A      ,		env=self.get_env()     )

				_UpperCAmelCase    :							Union[str, Any]									=					os.listdir(_A     )
				_UpperCAmelCase    :							Dict									=					{os.path.basename(_A     ) for p in contents}
				_UpperCAmelCase    :							str									=					[p for p in contents if p.endswith("""ckpt"""     )]
				assert len(_A     ) > 0

				self.assertIn("""test_generations.txt"""      ,		_A     )
				self.assertIn("""test_results.txt"""      ,		_A     )

				# get the following from the module, (we don't have access to `model` here)
				_UpperCAmelCase    :							List[str]									=					os.path.join(_A      ,		"""metrics.json"""     )
				_UpperCAmelCase    :							List[Any]									=					"""rouge2"""

				_UpperCAmelCase    :							List[Any]									=					load_json(_A     )
				# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
				print(_A     )
				_UpperCAmelCase    :							str									=					metrics["""val"""][-1]
				self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]      ,		0.01     )
				self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']      ,		_A     )
				self.assertEqual(len(metrics["""test"""]     )      ,		1     )
				_UpperCAmelCase    :							Optional[int]									=					int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
				self.assertEqual(len(metrics["""val"""]     )      ,		_A     )





