

"""simple docstring"""




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch





class 			__UpperCamelCase :


		def __init__(self :		Tuple     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=1_4     ,     __SCREAMING_SNAKE_CASE :		List[str]=7     ,     __SCREAMING_SNAKE_CASE :		Any=True     ,     __SCREAMING_SNAKE_CASE :		List[str]=True     ,     __SCREAMING_SNAKE_CASE :		str=False     ,     __SCREAMING_SNAKE_CASE :		str=True     ,     __SCREAMING_SNAKE_CASE :		Any=9_9     ,     __SCREAMING_SNAKE_CASE :		List[str]=3_2     ,     __SCREAMING_SNAKE_CASE :		Any=5     ,     __SCREAMING_SNAKE_CASE :		str=4     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=3_7     ,     __SCREAMING_SNAKE_CASE :		Any="gelu"     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0.1     ,     __SCREAMING_SNAKE_CASE :		Tuple=0.1     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=5_1_2     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=0.0_2     ,     ):
							A			=					parent
							A			=					batch_size
							A			=					seq_length
							A			=					is_training
							A			=					use_input_mask
							A			=					use_token_type_ids
							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			=					initializer_range
							A			=					None
							A			=					vocab_size - 1
							A			=					vocab_size - 1
							A			=					vocab_size - 1


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size)

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

							A			=					GPTaConfig(
							    vocab_size=self.vocab_size     ,     n_embd=self.hidden_size     ,     n_layer=self.num_hidden_layers     ,     n_head=self.num_attention_heads     ,     n_positions=self.max_position_embeddings     ,     use_cache=__SCREAMING_SNAKE_CASE     ,     bos_token_id=self.bos_token_id     ,     eos_token_id=self.eos_token_id     ,     pad_token_id=self.pad_token_id     ,     )

							return (config, input_ids, input_mask)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					self.prepare_config_and_inputs()
							A			,    A			,    A			=					config_and_inputs
							A			=					{"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			,    A			,    A			=					self.prepare_config_and_inputs()

							A			=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
							A			=					ids_tensor([self.batch_size, self.seq_length]     ,     vocab_size=2)

							return (
							    config,
							    input_ids,
							    attention_mask,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					2_0
							A			=					model_class_name(__SCREAMING_SNAKE_CASE)

							A			=					model.init_cache(input_ids.shape[0]     ,     __SCREAMING_SNAKE_CASE)
							A			=					jnp.ones((input_ids.shape[0], max_decoder_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=__SCREAMING_SNAKE_CASE     ,     past_key_values=__SCREAMING_SNAKE_CASE     ,     position_ids=__SCREAMING_SNAKE_CASE     ,     )

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

							A			=					model(__SCREAMING_SNAKE_CASE)

							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 :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple):
							A			=					2_0
							A			=					model_class_name(__SCREAMING_SNAKE_CASE)

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

							A			=					model.init_cache(input_ids.shape[0]     ,     __SCREAMING_SNAKE_CASE)
							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=__SCREAMING_SNAKE_CASE     ,     past_key_values=__SCREAMING_SNAKE_CASE     ,     position_ids=__SCREAMING_SNAKE_CASE     ,     )
							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=__SCREAMING_SNAKE_CASE     ,     position_ids=__SCREAMING_SNAKE_CASE     ,     )

							A			=					model(__SCREAMING_SNAKE_CASE     ,     attention_mask=__SCREAMING_SNAKE_CASE)

							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 			__UpperCamelCase (   _A     ,		_A     ,		unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
		SCREAMING_SNAKE_CASE								=    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					FlaxGPTaModelTester(self)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							for model_class_name in self.all_model_classes:
												A			,    A			,    A			=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							for model_class_name in self.all_model_classes:
												A			,    A			,    A			=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					GPTaTokenizer.from_pretrained("gpt2"     ,     pad_token="</s>"     ,     padding_side="left")
							A			=					tokenizer(["Hello this is a long string", "Hey"]     ,     return_tensors="np"     ,     padding=__SCREAMING_SNAKE_CASE     ,     truncation=__SCREAMING_SNAKE_CASE)

							A			=					FlaxGPTaLMHeadModel.from_pretrained("gpt2")
							A			=					False
							A			=					model.config.eos_token_id

							A			=					jax.jit(model.generate)

							A			=					jit_generate(inputs["input_ids"]     ,     attention_mask=inputs["attention_mask"]).sequences

							A			=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,     skip_special_tokens=__SCREAMING_SNAKE_CASE)

							A			=					[
							    "Hello this is a long string of words. I'm going to start with the first one.\n",
							    "Hey, I'm not sure if I'm going to be able to do",
							]

							self.assertListEqual(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		@is_pt_flax_cross_test
		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			,    A			=					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
																	A			=					self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
																	A			=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

																	A			=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,     __SCREAMING_SNAKE_CASE)
																	A			=					fx_state

																	with torch.no_grad():
																						A			=					pt_model(**__SCREAMING_SNAKE_CASE).to_tuple()

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

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

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


		@is_pt_flax_cross_test
		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			,    A			=					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
																	A			=					self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
																	A			=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

																	A			=					pt_model_class(__SCREAMING_SNAKE_CASE).eval()
																	A			=					model_class(__SCREAMING_SNAKE_CASE     ,     dtype=jnp.floataa)

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

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

																	with torch.no_grad():
																						A			=					pt_model(**__SCREAMING_SNAKE_CASE).to_tuple()

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

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

																	with torch.no_grad():
																						A			=					pt_model_loaded(**__SCREAMING_SNAKE_CASE).to_tuple()

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


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							for model_class_name in self.all_model_classes:
												A			=					model_class_name.from_pretrained("gpt2"     ,     from_pt=__SCREAMING_SNAKE_CASE)
												A			=					model(np.ones((1, 1)))
												self.assertIsNotNone(__SCREAMING_SNAKE_CASE)








"""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
__A     :    List[str]    =		'src/diffusers'
__A     :    Optional[int]    =		'docs/source/en'
__A     :    Union[str, Any]    =		'.'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							"r"			,							encoding="utf-8"			,							newline="\n"      ) as f:
										A			=					f.readlines()
					# Find the start prompt.
					A			=					0
					while not lines[start_index].startswith(lowercase__      ):
										start_index += 1
					start_index += 1

					A			=					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 |
__A     :    Any    =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A     :    Union[str, Any]    =		re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A     :    int    =		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.
__A     :    Any    =		re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__A     :    Optional[int]    =		importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__A     :    Tuple    =		spec.loader.load_module()
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,							lowercase__      )
					return [m.group(0      ) for m in matches]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					2 if text == "✅" or text == "❌" else len(lowercase__      )
					A			=					(width - text_length) // 2
					A			=					width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					A			=					{
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					A			=					{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.
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )

					# Let's lookup through all diffusers object (once).
					for attr_name in dir(lowercase__      ):
										A			=					None
										if attr_name.endswith("Tokenizer"      ):
															A			=					slow_tokenizers
															A			=					attr_name[:-9]
										elif attr_name.endswith("TokenizerFast"      ):
															A			=					fast_tokenizers
															A			=					attr_name[:-13]
										elif _re_tf_models.match(lowercase__      ) is not None:
															A			=					tf_models
															A			=					_re_tf_models.match(lowercase__      ).groups()[0]
										elif _re_flax_models.match(lowercase__      ) is not None:
															A			=					flax_models
															A			=					_re_flax_models.match(lowercase__      ).groups()[0]
										elif _re_pt_models.match(lowercase__      ) is not None:
															A			=					pt_models
															A			=					_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():
																									A			=					True
																									break
																				# Try again after removing the last word in the name
																				A			=					"".join(camel_case_split(lowercase__      )[:-1]      )

    # Let's build that table!
					A			=					list(model_name_to_config.keys()      )
					model_names.sort(key=str.lower      )
					A			=					["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).
					A			=					[len(lowercase__      ) + 2 for c in columns]
					A			=					max([len(lowercase__      ) for name in model_names]      ) + 2

					# Build the table per se
					A			=					"|" + "|".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"

					A			=					{True: "✅", False: "❌"}
					for name in model_names:
										A			=					model_name_to_prefix[name]
										A			=					[
										    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    __SCREAMING_SNAKE_CASE						(						lowercase__=False      ):
					"""simple docstring"""
					A			,    A			,    A			,    A			=					_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-->"			,							)
					A			=					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__":
	__A     :    Any    =		argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	__A     :    str    =		parser.parse_args()

	check_model_table(args.fix_and_overwrite)





