





import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


_a						=   get_logger(__name__)


_a						=   Path(__file__).parent / "model_card_template.md"
_a						=   uuida().hex
_a						=   os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
_a						=   os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
_a						=   HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"





def lowerCAmelCase__(__snake_case = None							)       ->   str:







	'''simple docstring'''







	lowerCamelCase__              =	F'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
	if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
		return ua + "; telemetry/off"
	if is_torch_available():
		ua += F'; torch/{_torch_version}'
	if is_flax_available():
		ua += F'; jax/{_jax_version}'
		ua += F'; flax/{_flax_version}'
	if is_onnx_available():
		ua += F'; onnxruntime/{_onnxruntime_version}'
	# CI will set this value to True
	if os.environ.get('''DIFFUSERS_IS_CI''' ,''''''							).upper() in ENV_VARS_TRUE_VALUES:
		ua += "; is_ci/true"
	if isinstance(__snake_case ,__snake_case							):
		ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()							)
	elif isinstance(__snake_case ,__snake_case							):
		ua += "; " + user_agent
	return ua





def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None							)       ->   Union[str, Any]:







	'''simple docstring'''







	if token is None:
		lowerCamelCase__              =	HfFolder.get_token()
	if organization is None:
		lowerCamelCase__              =	whoami(__snake_case							)['''name''']
		return F'{username}/{model_id}'
	else:
		return F'{organization}/{model_id}'





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







	if not is_jinja_available():
		raise ValueError(
		    '''Modelcard rendering is based on Jinja templates.'''
		    ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
		    ''' To install it, please run `pip install Jinja2`.'''							)

	if hasattr(__snake_case ,'''local_rank'''							) and args.local_rank not in [-1, 0]:
		return

	lowerCamelCase__              =	args.hub_token if hasattr(__snake_case ,'''hub_token'''							) else None
	lowerCamelCase__              =	get_full_repo_name(__snake_case ,token=__snake_case							)

	lowerCamelCase__              =	ModelCard.from_template(
	    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
	        language='''en''' ,license='''apache-2.0''' ,library_name='''diffusers''' ,tags=[] ,datasets=args.dataset_name ,metrics=[] ,) ,template_path=__snake_case ,model_name=__snake_case ,repo_name=__snake_case ,dataset_name=args.dataset_name if hasattr(__snake_case ,'''dataset_name'''							) else None ,learning_rate=args.learning_rate ,train_batch_size=args.train_batch_size ,eval_batch_size=args.eval_batch_size ,gradient_accumulation_steps=(
	        args.gradient_accumulation_steps if hasattr(__snake_case ,'''gradient_accumulation_steps'''							) else None
	    ) ,adam_betaa=args.adam_betaa if hasattr(__snake_case ,'''adam_beta1'''							) else None ,adam_betaa=args.adam_betaa if hasattr(__snake_case ,'''adam_beta2'''							) else None ,adam_weight_decay=args.adam_weight_decay if hasattr(__snake_case ,'''adam_weight_decay'''							) else None ,adam_epsilon=args.adam_epsilon if hasattr(__snake_case ,'''adam_epsilon'''							) else None ,lr_scheduler=args.lr_scheduler if hasattr(__snake_case ,'''lr_scheduler'''							) else None ,lr_warmup_steps=args.lr_warmup_steps if hasattr(__snake_case ,'''lr_warmup_steps'''							) else None ,ema_inv_gamma=args.ema_inv_gamma if hasattr(__snake_case ,'''ema_inv_gamma'''							) else None ,ema_power=args.ema_power if hasattr(__snake_case ,'''ema_power'''							) else None ,ema_max_decay=args.ema_max_decay if hasattr(__snake_case ,'''ema_max_decay'''							) else None ,mixed_precision=args.mixed_precision ,)

	lowerCamelCase__              =	os.path.join(args.output_dir ,'''README.md'''							)
	model_card.save(__snake_case							)





def lowerCAmelCase__(__snake_case ,__snake_case = None							)       ->   str:







	'''simple docstring'''







	if resolved_file is None or commit_hash is not None:
		return commit_hash
	lowerCamelCase__              =	str(Path(__snake_case							).as_posix()							)
	lowerCamelCase__              =	re.search(R'''snapshots/([^/]+)/''' ,__snake_case							)
	if search is None:
		return None
	lowerCamelCase__              =	search.groups()[0]
	return commit_hash if REGEX_COMMIT_HASH.match(__snake_case							) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_a						=   os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_a						=   os.path.join(hf_cache_home, "diffusers")





def lowerCAmelCase__(__snake_case = None ,__snake_case = None							)       ->   None:







	'''simple docstring'''







	if new_cache_dir is None:
		lowerCamelCase__              =	DIFFUSERS_CACHE
	if old_cache_dir is None:
		lowerCamelCase__              =	old_diffusers_cache

	lowerCamelCase__              =	Path(__snake_case							).expanduser()
	lowerCamelCase__              =	Path(__snake_case							).expanduser()
	for old_blob_path in old_cache_dir.glob('''**/blobs/*'''							):
		if old_blob_path.is_file() and not old_blob_path.is_symlink():
			lowerCamelCase__              =	new_cache_dir / old_blob_path.relative_to(__snake_case							)
			new_blob_path.parent.mkdir(parents=__snake_case ,exist_ok=__snake_case							)
			os.replace(__snake_case ,__snake_case							)
			try:
				os.symlink(__snake_case ,__snake_case							)
			except OSError:
				logger.warning(
				    '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''							)
	# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


_a						=   os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
			_a						=   0
else:
			with open(cache_version_file) as f:
						try:
									_a						=   int(f.read())
						except ValueError:
									_a						=   0

if cache_version < 1:
			_a						=   os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
			if old_cache_is_not_empty:
						logger.warning(
						    "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
						    "existing cached models. This is a one-time operation, you can interrupt it or run it "
						    "later by calling `diffusers.utils.hub_utils.move_cache()`."
						)
						try:
									move_cache()
						except Exception as e:
									_a						=   "\n".join(traceback.format_tb(e.__traceback__))
									logger.error(
									    f"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
									    "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
									    "message and we will do our best to help."
									)

if cache_version < 1:
			try:
						os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
						with open(cache_version_file, "w") as f:
									f.write("1")
			except Exception:
						logger.warning(
						    f"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
						    "the directory exists and can be written to."
						)





def lowerCAmelCase__(__snake_case ,__snake_case = None							)       ->   str:







	'''simple docstring'''







	if variant is not None:
		lowerCamelCase__              =	weights_name.split('''.'''							)
		lowerCamelCase__              =	splits[:-1] + [variant] + splits[-1:]
		lowerCamelCase__              =	'''.'''.join(__snake_case							)

	return weights_name





def lowerCAmelCase__(__snake_case ,*,
    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case=None ,)       ->   List[str]:







	'''simple docstring'''







	lowerCamelCase__              =	str(__snake_case							)
	if os.path.isfile(__snake_case							):
		return pretrained_model_name_or_path
	elif os.path.isdir(__snake_case							):
		if os.path.isfile(os.path.join(__snake_case ,__snake_case							)							):
			# Load from a PyTorch checkpoint
			lowerCamelCase__              =	os.path.join(__snake_case ,__snake_case							)
			return model_file
		elif subfolder is not None and os.path.isfile(
		    os.path.join(__snake_case ,__snake_case ,__snake_case							)							):
			lowerCamelCase__              =	os.path.join(__snake_case ,__snake_case ,__snake_case							)
			return model_file
		else:
			raise EnvironmentError(
			    F'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'							)
	else:
		# 1. First check if deprecated way of loading from branches is used
		if (
		    revision in DEPRECATED_REVISION_ARGS
		    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
		    and version.parse(version.parse(__snake_case							).base_version							) >= version.parse('''0.20.0'''							)
		):
			try:
				lowerCamelCase__              =	hf_hub_download(
				    __snake_case ,filename=_add_variant(__snake_case ,__snake_case							) ,cache_dir=__snake_case ,force_download=__snake_case ,proxies=__snake_case ,resume_download=__snake_case ,local_files_only=__snake_case ,use_auth_token=__snake_case ,user_agent=__snake_case ,subfolder=__snake_case ,revision=revision or commit_hash ,)
				warnings.warn(
				    F'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.' ,__snake_case ,)
				return model_file
			except:  # noqa: E722
				warnings.warn(
				    F'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__snake_case ,__snake_case							)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(__snake_case ,__snake_case							)}\' so that the correct variant file can be added.' ,__snake_case ,)
		try:
			# 2. Load model file as usual
			lowerCamelCase__              =	hf_hub_download(
			    __snake_case ,filename=__snake_case ,cache_dir=__snake_case ,force_download=__snake_case ,proxies=__snake_case ,resume_download=__snake_case ,local_files_only=__snake_case ,use_auth_token=__snake_case ,user_agent=__snake_case ,subfolder=__snake_case ,revision=revision or commit_hash ,)
			return model_file

		except RepositoryNotFoundError:
			raise EnvironmentError(
			    F'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
			    '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
			    '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
			    '''login`.'''							)
		except RevisionNotFoundError:
			raise EnvironmentError(
			    F'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
			    '''this model name. Check the model page at '''
			    F'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'							)
		except EntryNotFoundError:
			raise EnvironmentError(
			    F'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'							)
		except HTTPError as err:
			raise EnvironmentError(
			    F'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'							)
		except ValueError:
			raise EnvironmentError(
			    F'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
			    F' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
			    F' directory containing a file named {weights_name} or'
			    ''' \nCheckout your internet connection or see how to run the library in'''
			    ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''							)
		except EnvironmentError:
			raise EnvironmentError(
			    F'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
			    '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
			    F'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
			    F'containing a file named {weights_name}'							)










from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
			import numpy
			import tensorflow as tf

			from transformers.models.esm.modeling_tf_esm import (
			    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFEsmForMaskedLM,
			    TFEsmForSequenceClassification,
			    TFEsmForTokenClassification,
			    TFEsmModel,
			)






class       __A					:

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	parent
				lowerCamelCase__              =	1_3
				lowerCamelCase__              =	7
				lowerCamelCase__              =	True
				lowerCamelCase__              =	True
				lowerCamelCase__              =	True
				lowerCamelCase__              =	9_9
				lowerCamelCase__              =	3_2
				lowerCamelCase__              =	2
				lowerCamelCase__              =	4
				lowerCamelCase__              =	3_7
				lowerCamelCase__              =	'''gelu'''
				lowerCamelCase__              =	0.1
				lowerCamelCase__              =	0.1
				lowerCamelCase__              =	5_1_2
				lowerCamelCase__              =	1_6
				lowerCamelCase__              =	2
				lowerCamelCase__              =	0.02
				lowerCamelCase__              =	3
				lowerCamelCase__              =	4
				lowerCamelCase__              =	None





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

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

				lowerCamelCase__              =	None
				lowerCamelCase__              =	None
				lowerCamelCase__              =	None
				if self.use_labels:
					lowerCamelCase__              =	ids_tensor([self.batch_size] ,						self.type_sequence_label_size							)
					lowerCamelCase__              =	ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels							)
					lowerCamelCase__              =	ids_tensor([self.batch_size] ,						self.num_choices							)

				lowerCamelCase__              =	EsmConfig(
				    vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						pad_token_id=1 ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						initializer_range=self.initializer_range ,						)

				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				(
				    (
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			
				)              =	self.prepare_config_and_inputs()

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

				return (
				    config,
				    input_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	TFEsmModel(config=__lowerCAmelCase							)
				lowerCamelCase__              =	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				lowerCamelCase__              =	model(__lowerCAmelCase							)

				lowerCamelCase__              =	[input_ids, input_mask]
				lowerCamelCase__              =	model(__lowerCAmelCase							)

				lowerCamelCase__              =	model(__lowerCAmelCase							)

				self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	True

				lowerCamelCase__              =	TFEsmModel(config=__lowerCAmelCase							)
				lowerCamelCase__              =	{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''encoder_hidden_states''': encoder_hidden_states,
				    '''encoder_attention_mask''': encoder_attention_mask,
				}
				lowerCamelCase__              =	model(__lowerCAmelCase							)

				lowerCamelCase__              =	[input_ids, input_mask]
				lowerCamelCase__              =	model(__lowerCAmelCase ,						encoder_hidden_states=__lowerCAmelCase							)

				# Also check the case where encoder outputs are not passed
				lowerCamelCase__              =	model(__lowerCAmelCase ,						attention_mask=__lowerCAmelCase							)

				self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	TFEsmForMaskedLM(config=__lowerCAmelCase							)
				lowerCamelCase__              =	model([input_ids, input_mask]							)
				self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.num_labels
				lowerCamelCase__              =	TFEsmForTokenClassification(config=__lowerCAmelCase							)
				lowerCamelCase__              =	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				lowerCamelCase__              =	model(__lowerCAmelCase							)
				self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.prepare_config_and_inputs()
				(
				    (
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			
				)              =	config_and_inputs
				lowerCamelCase__              =	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict






@require_tf
class       __A					(					lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		(
			    (
			        TFEsmModel,
			        TFEsmForMaskedLM,
			        TFEsmForSequenceClassification,
			        TFEsmForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCAmelCase_							  =		(
			    {
			        """feature-extraction""": TFEsmModel,
			        """fill-mask""": TFEsmForMaskedLM,
			        """text-classification""": TFEsmForSequenceClassification,
			        """token-classification""": TFEsmForTokenClassification,
			        """zero-shot""": TFEsmForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	TFEsmModelTester(self							)
				lowerCamelCase__              =	ConfigTester(self ,						config_class=__lowerCAmelCase ,						hidden_size=3_7							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase							)





			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__              =	TFEsmModel.from_pretrained(__lowerCAmelCase							)
					self.assertIsNotNone(__lowerCAmelCase							)





			@unittest.skip('''Protein models do not support embedding resizing.'''							)
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				pass





			@unittest.skip('''Protein models do not support embedding resizing.'''							)
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				pass





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					lowerCamelCase__              =	model_class(__lowerCAmelCase							)
					assert isinstance(model.get_input_embeddings() ,						tf.keras.layers.Layer							)
					if model_class is TFEsmForMaskedLM:
						# Output embedding test differs from the main test because they're a matrix, not a layer
						lowerCamelCase__              =	model.get_bias()
						assert isinstance(__lowerCAmelCase ,						__lowerCAmelCase							)
						for k, v in name.items():
							assert isinstance(__lowerCAmelCase ,						tf.Variable							)
					else:
						lowerCamelCase__              =	model.get_output_embeddings()
						assert x is None
						lowerCamelCase__              =	model.get_bias()
						assert name is None






@require_tf
class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''							)

				lowerCamelCase__              =	tf.constant([[0, 1, 2, 3, 4, 5]]							)
				lowerCamelCase__              =	model(__lowerCAmelCase							)[0]
				lowerCamelCase__              =	[1, 6, 3_3]
				self.assertEqual(list(output.numpy().shape							) ,						__lowerCAmelCase							)
				# compare the actual values for a slice.
				lowerCamelCase__              =	tf.constant(
				    [
				        [
				            [8.92_1518, -10.58_9814, -6.467_1307],
				            [-6.396_7156, -13.91_1377, -1.121_1915],
				            [-7.78_1247, -13.95_1557, -3.74_0592],
				        ]
				    ]							)
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() ,						expected_slice.numpy() ,						atol=1E-2							)							)





			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''							)

				lowerCamelCase__              =	tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]							)
				lowerCamelCase__              =	model(__lowerCAmelCase							)[0]
				# compare the actual values for a slice.
				lowerCamelCase__              =	tf.constant(
				    [
				        [
				            [0.1444_3092, 0.5412_5327, 0.324_7739],
				            [0.3034_0484, 0.0052_6676, 0.3107_7722],
				            [0.3227_8043, -0.2498_7096, 0.341_4628],
				        ]
				    ]							)
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() ,						expected_slice.numpy() ,						atol=1E-4							)							)



