'''simple docstring'''


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
__lowerCAmelCase	:       Dict						       =	"src/transformers"
__lowerCAmelCase	:       List[Any]						       =	"docs/source/en"
__lowerCAmelCase	:       Tuple						       =	"."





def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	with open(UpperCamelCase__    ,					'''r'''    ,					encoding='''utf-8'''    ,					newline='''\n'''       ) as f:
		__UpperCAmelCase							=			f.readlines()
	# Find the start prompt.
	__UpperCAmelCase							=			0
	while not lines[start_index].startswith(UpperCamelCase__       ):
		start_index += 1
	start_index += 1

	__UpperCAmelCase							=			start_index
	while not lines[end_index].startswith(UpperCamelCase__       ):
		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 |
__lowerCAmelCase	:       int						       =	"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase	:       Tuple						       =	re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__lowerCAmelCase	:       Tuple						       =	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.
__lowerCAmelCase	:       List[Any]						       =	re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase	:       int						       =	direct_transformers_import(TRANSFORMERS_PATH)





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,					UpperCamelCase__       )
	return [m.group(0       ) for m in matches]





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			2 if text == '''✅''' or text == '''❌''' else len(UpperCamelCase__       )
	__UpperCAmelCase							=			(width - text_length) // 2
	__UpperCAmelCase							=			width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__UpperCAmelCase							=			{
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	__UpperCAmelCase							=			{name: config.replace('''Config'''    ,					''''''       ) 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(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )

	# Let's lookup through all transformers object (once).
	for attr_name in dir(UpperCamelCase__       ):
		__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[:-1_3]
		elif _re_tf_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			tf_models
			__UpperCAmelCase							=			_re_tf_models.match(UpperCamelCase__       ).groups()[0]
		elif _re_flax_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			flax_models
			__UpperCAmelCase							=			_re_flax_models.match(UpperCamelCase__       ).groups()[0]
		elif _re_pt_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			pt_models
			__UpperCAmelCase							=			_re_pt_models.match(UpperCamelCase__       ).groups()[0]

		if lookup_dict is not None:
			while len(UpperCamelCase__       ) > 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(UpperCamelCase__       )[:-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(UpperCamelCase__       ) + 2 for c in columns]
	__UpperCAmelCase							=			max([len(UpperCamelCase__       ) for name in model_names]       ) + 2

	# Build the table per se
	__UpperCAmelCase							=			'''|''' + '''|'''.join([_center_text(UpperCamelCase__    ,					UpperCamelCase__       ) for c, w in zip(UpperCamelCase__    ,					UpperCamelCase__       )]       ) + '''|\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(UpperCamelCase__    ,					UpperCamelCase__       ) for l, w in zip(UpperCamelCase__    ,					UpperCamelCase__       )]       ) + "|\n"
	return table





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]=False       ):


	"""simple docstring"""
	__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			_find_text_in_file(
	    filename=os.path.join(UpperCamelCase__    ,					'''index.md'''       )    ,					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(UpperCamelCase__    ,					'''index.md'''       )    ,					'''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.md` has not been updated. Run `make fix-copies` to fix this.'''       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Optional[int]						       =	argparse.ArgumentParser()
					parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
					__lowerCAmelCase	:       Optional[Any]						       =	parser.parse_args()

					check_model_table(args.fix_and_overwrite)

'''simple docstring'''


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


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






@slow
@require_flax
class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							List[Any]     ,		__a				:							List[str]     ,		__a				:							Optional[Any]	)							->	List[Any]:
								return f"""gaussian_noise_s={seed}_shape={'_'.join([str(__a	) for s in shape]	)}.npy"""





							def       snake_case__						(      self				:							Dict	)							->	Union[str, Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Tuple=0     ,		__a				:							List[Any]=(4, 4, 6_4, 6_4)     ,		__a				:							Optional[Any]=False	)							->	Tuple:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			jnp.array(load_hf_numpy(self.get_file_format(__a     ,		__a	)	)     ,		dtype=__a	)
								return image





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]=False     ,		__a				:							Optional[Any]="CompVis/stable-diffusion-v1-4"	)							->	Any:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			'''bf16''' if fpaa else None

								__UpperCAmelCase				,			__UpperCAmelCase							=			FlaxUNetaDConditionModel.from_pretrained(
								    __a     ,		subfolder='''unet'''     ,		dtype=__a     ,		revision=__a	)
								return model, params





							def       snake_case__						(      self				:							str     ,		__a				:							int=0     ,		__a				:							Tuple=(4, 7_7, 7_6_8)     ,		__a				:							Optional[int]=False	)							->	Union[str, Any]:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			jnp.array(load_hf_numpy(self.get_file_format(__a     ,		__a	)	)     ,		dtype=__a	)
								return hidden_states





							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
							        [1_7, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
							        [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
							        [3, 1_0_0_0, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
							        # fmt: on
							    ]	)
							def       snake_case__						(      self				:							Tuple     ,		__a				:							Tuple     ,		__a				:							str     ,		__a				:							Optional[Any]	)							->	Any:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_latents(__a     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_encoder_hidden_states(__a     ,		fpaa=__a	)

								__UpperCAmelCase							=			model.apply(
								    {'''params''': params}     ,		__a     ,		jnp.array(__a     ,		dtype=jnp.intaa	)     ,		encoder_hidden_states=__a     ,		).sample

								assert sample.shape == latents.shape

								__UpperCAmelCase							=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)     ,		dtype=jnp.floataa	)
								__UpperCAmelCase							=			jnp.array(__a     ,		dtype=jnp.floataa	)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(__a     ,		__a     ,		atol=1e-2	)






							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
							        [1_7, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
							        [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
							        [3, 1_0_0_0, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
							        # fmt: on
							    ]	)
							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Optional[int]     ,		__a				:							Optional[Any]     ,		__a				:							Optional[Any]	)							->	Union[str, Any]:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_latents(__a     ,		shape=(4, 4, 9_6, 9_6)     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_encoder_hidden_states(__a     ,		shape=(4, 7_7, 1_0_2_4)     ,		fpaa=__a	)

								__UpperCAmelCase							=			model.apply(
								    {'''params''': params}     ,		__a     ,		jnp.array(__a     ,		dtype=jnp.intaa	)     ,		encoder_hidden_states=__a     ,		).sample

								assert sample.shape == latents.shape

								__UpperCAmelCase							=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)     ,		dtype=jnp.floataa	)
								__UpperCAmelCase							=			jnp.array(__a     ,		dtype=jnp.floataa	)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(__a     ,		__a     ,		atol=1e-2	)
