



"""simple docstring"""


import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case_								=			"""src/diffusers"""
snake_case_								=			"""docs/source/en"""
snake_case_								=			"""."""

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_		):
						with open(lowercase_						,      'r'						,      encoding='utf-8'						,      newline='\n'		) as f:
												UpperCAmelCase			 =					f.readlines()
						# Find the start prompt.
						UpperCAmelCase			 =					0
						while not lines[start_index].startswith(lowercase_		):
												start_index += 1
						start_index += 1

						UpperCAmelCase			 =					start_index
						while not lines[end_index].startswith(lowercase_		):
												end_index += 1
						end_index -= 1

						while len(lines[start_index]		) <= 1:
												start_index += 1
						while len(lines[end_index]		) <= 1:
												end_index -= 1
						end_index += 1
						return "".join(lines[start_index:end_index]		), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
snake_case_								=			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
snake_case_								=			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
snake_case_								=			re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case_								=			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
snake_case_								=			importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
snake_case_								=			spec.loader.load_module()

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'						,      lowercase_		)
						return [m.group(0		) for m in matches]

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					2 if text == '✅' or text == '❌' else len(lowercase_		)
						UpperCAmelCase			 =					(width - text_length) // 2
						UpperCAmelCase			 =					width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent

def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						UpperCAmelCase			 =					{
						    name: config_mapping_names[code]
						    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_mapping_names
						}
						UpperCAmelCase			 =					{name: config.replace('ConfigMixin'						,      ''		) for name, config in model_name_to_config.items()}

						# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)
						UpperCAmelCase			 =					collections.defaultdict(lowercase_		)

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(lowercase_		):
												UpperCAmelCase			 =					None
												if attr_name.endswith('Tokenizer'		):
																		UpperCAmelCase			 =					slow_tokenizers
																		UpperCAmelCase			 =					attr_name[:-9]
												elif attr_name.endswith('TokenizerFast'		):
																		UpperCAmelCase			 =					fast_tokenizers
																		UpperCAmelCase			 =					attr_name[:-13]
												elif _re_tf_models.match(lowercase_		) is not None:
																		UpperCAmelCase			 =					tf_models
																		UpperCAmelCase			 =					_re_tf_models.match(lowercase_		).groups()[0]
												elif _re_flax_models.match(lowercase_		) is not None:
																		UpperCAmelCase			 =					flax_models
																		UpperCAmelCase			 =					_re_flax_models.match(lowercase_		).groups()[0]
												elif _re_pt_models.match(lowercase_		) is not None:
																		UpperCAmelCase			 =					pt_models
																		UpperCAmelCase			 =					_re_pt_models.match(lowercase_		).groups()[0]

												if lookup_dict is not None:
																		while len(lowercase_		) > 0:
																								if attr_name in model_name_to_prefix.values():
																														UpperCAmelCase			 =					True
																														break
																								# Try again after removing the last word in the name
																								UpperCAmelCase			 =					''.join(camel_case_split(lowercase_		)[:-1]		)

    # Let's build that table!
						UpperCAmelCase			 =					list(model_name_to_config.keys()		)
						model_names.sort(key=str.lower		)
						UpperCAmelCase			 =					['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
						# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
						UpperCAmelCase			 =					[len(lowercase_		) + 2 for c in columns]
						UpperCAmelCase			 =					max([len(lowercase_		) for name in model_names]		) + 2

						# Build the table per se
						UpperCAmelCase			 =					'|' + '|'.join([_center_text(lowercase_						,      lowercase_		) for c, w in zip(lowercase_						,      lowercase_		)]		) + '|\n'
						# Use ":-----:" format to center-aligned table cell texts
						table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]		) + "|\n"

						UpperCAmelCase			 =					{True: '✅', False: '❌'}
						for name in model_names:
												UpperCAmelCase			 =					model_name_to_prefix[name]
												UpperCAmelCase			 =					[
												    name,
												    check[slow_tokenizers[prefix]],
												    check[fast_tokenizers[prefix]],
												    check[pt_models[prefix]],
												    check[tf_models[prefix]],
												    check[flax_models[prefix]],
												]
												table += "|" + "|".join([_center_text(lowercase_						,      lowercase_		) for l, w in zip(lowercase_						,      lowercase_		)]		) + "|\n"
						return table

def 					_lowerCAmelCase (  lowercase_=False		):
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					_find_text_in_file(
						    filename=os.path.join(lowercase_						,      'index.mdx'		)						,      start_prompt='<!--This table is updated automatically from the auto modules'						,      end_prompt='<!-- End table-->'						,      )
						UpperCAmelCase			 =					get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(lowercase_						,      'index.mdx'		)						,      'w'						,      encoding='utf-8'						,      newline='\n'		) as f:
																								f.writelines(lines[:start_index] + [new_table] + lines[end_index:]		)
												else:
																		raise ValueError(
																		    'The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'		)


if __name__ == "__main__":
				snake_case_								=			argparse.ArgumentParser()
				parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
				snake_case_								=			parser.parse_args()

				check_model_table(args.fix_and_overwrite)












'''simple docstring'''


from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel



@require_tf
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     MarianConfig
		__lowerCAmelCase								=     {}
		__lowerCAmelCase								=     '''gelu'''







		def __init__(self    :   Tuple   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict=13   ,   _lowerCAmelCase    :   Optional[int]=7   ,   _lowerCAmelCase    :   str=True   ,   _lowerCAmelCase    :   Optional[int]=False   ,   _lowerCAmelCase    :   Tuple=99   ,   _lowerCAmelCase    :   str=32   ,   _lowerCAmelCase    :   str=2   ,   _lowerCAmelCase    :   Union[str, Any]=4   ,   _lowerCAmelCase    :   Dict=37   ,   _lowerCAmelCase    :   int=0.1   ,   _lowerCAmelCase    :   List[Any]=0.1   ,   _lowerCAmelCase    :   Tuple=20   ,   _lowerCAmelCase    :   List[str]=2   ,   _lowerCAmelCase    :   Any=1   ,   _lowerCAmelCase    :   int=0   ,   ):
			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_dropout_prob
			A					=   attention_probs_dropout_prob
			A					=   max_position_embeddings
			A					=   eos_token_id
			A					=   pad_token_id
			A					=   bos_token_id







		def 							A  (self    :   int   ):
			A					=   ids_tensor([self.batch_size, self.seq_length - 1]   ,   self.vocab_size   )
			A					=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )   ,   1   )
			A					=   tf.concat([input_ids, eos_tensor]   ,   axis=1   )

			A					=   ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size   )

			A					=   self.config_cls(
			    vocab_size=self.vocab_size   ,   d_model=self.hidden_size   ,   encoder_layers=self.num_hidden_layers   ,   decoder_layers=self.num_hidden_layers   ,   encoder_attention_heads=self.num_attention_heads   ,   decoder_attention_heads=self.num_attention_heads   ,   encoder_ffn_dim=self.intermediate_size   ,   decoder_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_ids=[2]   ,   bos_token_id=self.bos_token_id   ,   pad_token_id=self.pad_token_id   ,   decoder_start_token_id=self.pad_token_id   ,   **self.config_updates   ,   )
			A					=   prepare_marian_inputs_dict(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
			return config, inputs_dict







		def 							A  (self    :   List[Any]   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   Any   ):
			A					=   TFMarianModel(config=_lowerCAmelCase   ).get_decoder()
			A					=   inputs_dict["""input_ids"""]

			A					=   input_ids[:1, :]
			A					=   inputs_dict["""attention_mask"""][:1, :]
			A					=   inputs_dict["""head_mask"""]
			A					=   1

			# first forward pass
			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   ,   head_mask=_lowerCAmelCase   ,   use_cache=_lowerCAmelCase   )

			A	,     A					=   outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			A					=   ids_tensor((self.batch_size, 3)   ,   config.vocab_size   )
			A					=   tf.cast(ids_tensor((self.batch_size, 3)   ,   2   )   ,   tf.inta   )

			# append to next input_ids and
			A					=   tf.concat([input_ids, next_tokens]   ,   axis=-1   )
			A					=   tf.concat([attention_mask, next_attn_mask]   ,   axis=-1   )

			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   )[0]
			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   ,   past_key_values=_lowerCAmelCase   )[0]

			self.parent.assertEqual(next_tokens.shape[1]   ,   output_from_past.shape[1]   )

			# select random slice
			A					=   int(ids_tensor((1,)   ,   output_from_past.shape[-1]   )   )
			A					=   output_from_no_past[:, -3:, random_slice_idx]
			A					=   output_from_past[:, :, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(_lowerCAmelCase   ,   _lowerCAmelCase   ,   rtol=1e-3   )






def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      )   ->List[str]:



	"""simple docstring"""







	if attention_mask is None:
		A					=   tf.cast(tf.math.not_equal(UpperCAmelCase							,      config.pad_token_id     )							,      tf.inta     )
	if decoder_attention_mask is None:
		A					=   tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape							,      dtype=tf.inta     ),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,      config.pad_token_id     )							,      tf.inta     ),
		    ]							,      axis=-1							,      )
	if head_mask is None:
		A					=   tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
	if decoder_head_mask is None:
		A					=   tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
	if cross_attn_head_mask is None:
		A					=   tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}



@require_tf
class 				__UpperCAmelCase	(							A__		,       A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
		__lowerCAmelCase								=     (TFMarianMTModel,) if is_tf_available() else ()
		__lowerCAmelCase								=     (
		    {
		        '''conversational''': TFMarianMTModel,
		        '''feature-extraction''': TFMarianModel,
		        '''summarization''': TFMarianMTModel,
		        '''text2text-generation''': TFMarianMTModel,
		        '''translation''': TFMarianMTModel,
		    }
		    if is_tf_available()
		    else {}
		)
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     False







		def 							A  (self    :   Tuple   ):
			A					=   TFMarianModelTester(self   )
			A					=   ConfigTester(self   ,   config_class=_lowerCAmelCase   )







		def 							A  (self    :   List[str]   ):
			self.config_tester.run_common_tests()







		def 							A  (self    :   Any   ):
			A					=   self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase   )






@require_tf
class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     10_00  # show more chars for failing integration tests







		@classmethod
		def 							A  (cls    :   Union[str, Any]   ):
			A					=   F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
			return cls







		@cached_property
		def 							A  (self    :   str   ):
			return AutoTokenizer.from_pretrained(self.model_name   )







		@property
		def 							A  (self    :   Optional[int]   ):
			return self.tokenizer.eos_token_id







		@cached_property
		def 							A  (self    :   Optional[Any]   ):
			warnings.simplefilter("""error"""   )
			A					=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
			assert isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   model.config
			self.assertListEqual(c.bad_words_ids   ,   [[c.pad_token_id]]   )
			self.assertEqual(c.max_length   ,   512   )
			self.assertEqual(c.decoder_start_token_id   ,   c.pad_token_id   )
			return model







		def 							A  (self    :   Optional[int]   ,   **_lowerCAmelCase    :   Any   ):
			A					=   self.translate_src_text(**_lowerCAmelCase   )
			self.assertListEqual(self.expected_text   ,   _lowerCAmelCase   )







		def 							A  (self    :   Union[str, Any]   ,   **_lowerCAmelCase    :   Union[str, Any]   ):
			A					=   self.tokenizer(self.src_text   ,   **_lowerCAmelCase   ,   padding=_lowerCAmelCase   ,   return_tensors="""tf"""   )
			A					=   self.model.generate(
			    model_inputs.input_ids   ,   attention_mask=model_inputs.attention_mask   ,   num_beams=2   ,   max_length=128   )
			A					=   self.tokenizer.batch_decode(generated_ids.numpy()   ,   skip_special_tokens=_lowerCAmelCase   )
			return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''mt'''
		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
		__lowerCAmelCase								=     ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   str   ):
			self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     '''zh'''
		__lowerCAmelCase								=     ['''My name is Wolfgang and I live in Berlin''']
		__lowerCAmelCase								=     ['''我叫沃尔夫冈 我住在柏林''']







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Any   ):
			self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     '''ROMANCE'''
		__lowerCAmelCase								=     [
		    '''>>fr<< Don\'t spend so much time watching TV.''',
		    '''>>pt<< Your message has been sent.''',
		    '''>>es<< He\'s two years older than me.''',
		]
		__lowerCAmelCase								=     [
		    '''Ne passez pas autant de temps à regarder la télé.''',
		    '''A sua mensagem foi enviada.''',
		    '''Es dos años más viejo que yo.''',
		]







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Union[str, Any]   ):
			self._assert_generated_batch_equal_expected()







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Union[str, Any]   ):
			A					=   TranslationPipeline(self.model   ,   self.tokenizer   ,   framework="""tf"""   )
			A					=   pipeline(self.src_text   )
			self.assertEqual(self.expected_text   ,   [x["""translation_text"""] for x in output]   )

