


import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
	import jax.numpy as jnp

	from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				@slow
				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
											snake_case__	  =       AutoTokenizer.from_pretrained("""xlm-roberta-base""")
											snake_case__	  =       """The dog is cute and lives in the garden house"""
											snake_case__	  =       jnp.array([tokenizer.encode(UpperCamelCase__)])

											snake_case__	  =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
											snake_case__	  =       jnp.array(
											    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]])

											snake_case__	  =       model(UpperCamelCase__)["""last_hidden_state"""]
											self.assertEqual(output.shape			,		UpperCamelCase__)
											# compare the actual values for a slice of last dim
											self.assertTrue(jnp.allclose(output[:, :, -1]			,		UpperCamelCase__			,		atol=1E-3))








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a__       =     {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""CLIPTokenizerFast"""]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""CLIPFeatureExtractor"""]
	a__       =     ["""CLIPImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CLIPModel""",
	    """CLIPPreTrainedModel""",
	    """CLIPTextModel""",
	    """CLIPTextModelWithProjection""",
	    """CLIPVisionModel""",
	    """CLIPVisionModelWithProjection""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFCLIPModel""",
	    """TFCLIPPreTrainedModel""",
	    """TFCLIPTextModel""",
	    """TFCLIPVisionModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """FlaxCLIPModel""",
	    """FlaxCLIPPreTrainedModel""",
	    """FlaxCLIPTextModel""",
	    """FlaxCLIPTextPreTrainedModel""",
	    """FlaxCLIPVisionModel""",
	    """FlaxCLIPVisionPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_clip import (
	    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPConfig,
	    CLIPOnnxConfig,
	    CLIPTextConfig,
	    CLIPVisionConfig,
	)
	from .processing_clip import CLIPProcessor
	from .tokenization_clip import CLIPTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_clip_fast import CLIPTokenizerFast

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clip import CLIPFeatureExtractor
		from .image_processing_clip import CLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clip import (
		    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPModel,
		    CLIPPreTrainedModel,
		    CLIPTextModel,
		    CLIPTextModelWithProjection,
		    CLIPVisionModel,
		    CLIPVisionModelWithProjection,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_clip import (
		    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCLIPModel,
		    TFCLIPPreTrainedModel,
		    TFCLIPTextModel,
		    TFCLIPVisionModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_clip import (
		    FlaxCLIPModel,
		    FlaxCLIPPreTrainedModel,
		    FlaxCLIPTextModel,
		    FlaxCLIPTextPreTrainedModel,
		    FlaxCLIPVisionModel,
		    FlaxCLIPVisionPreTrainedModel,
		)


else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
